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Sensors 2014, 14(11), 21247-21257; doi:10.3390/s141121247

Fast Human Detection for Intelligent Monitoring Using Surveillance Visible Sensors

Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea
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Received: 15 September 2014 / Revised: 25 October 2014 / Accepted: 30 October 2014 / Published: 11 November 2014
(This article belongs to the Section Physical Sensors)
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Abstract

Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This paper proposes fast human detection by selecting optimal levels of image scale using each level’s adaptive region-of-interest (ROI). To estimate the image-scaling level, we generate a Hough windows map (HWM) and select a few optimal image scales based on the strength of the HWM and the divide-and-conquer algorithm. Furthermore, adaptive ROIs are arranged per image scale to provide a different search area. We employ a cascade random forests classifier to separate candidate windows into human and nonhuman classes. The proposed algorithm has been successfully applied to real-world surveillance video sequences, and its detection accuracy and computational speed show a better performance than those of other related methods. View Full-Text
Keywords: human detection; Hough windows map; adaptive ROI; divide-and-conquer; CaRF human detection; Hough windows map; adaptive ROI; divide-and-conquer; CaRF
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ko, B.C.; Jeong, M.; Nam, J. Fast Human Detection for Intelligent Monitoring Using Surveillance Visible Sensors. Sensors 2014, 14, 21247-21257.

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